The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
A typical moving object detection system operates by a process that performs the following steps: (a) generating object trajectories from video with multiple motion objects under different lighting and occlusion conditions; (b) clustering trajectories into flow patterns and predicting one or more trajectories based on the flow patterns; (c) detecting one or more abnormal trajectories based on normal flow patterns; (d) analyzing the behavior of trajectories and creating one or more ontologies of behavior patterns; and (e) employing the ontology with a multiple camera tracking system.
The typical process involves processing of trajectory points using a transformation function that is independent of application contexts. As a result, these methods cannot be used to derive a detection function that will capture the intention of the motion trajectory with respect to the targets of interests in the field of view of camera and the criticality of a target relative to other targets when a moving object is approaching a target. Both of these contexts are important for security and marketing applications.
The deployed surveillance systems could be used for other purposes than only the surveillance purpose. Such multi purpose usage of the surveillance system requires an architecture and process which enables multiple applications to share the surveillance system resources (devices and various servers). This approach is beneficial for the user since the justification of the system purchase could be represented in real dollar terms.
Today's surveillance systems have a number of limitations. For example, accuracy is not satisfactory. Multiple tracking algorithms have been proposed and work under controlled environment. However, the accuracy has not reach to an acceptable level. Also, today's systems are difficult to set up and use. Since most of the methods do not yield high accuracy for all cases, it is necessary to select the situation and customize the parameters of the detection model based on the context. Further, the CPU intensive costs of today's systems limit the application of one tracking system to a limited number of cameras. Finally, today's systems are limited in adaptability because most of the systems cannot adapt to changing fields of views. This inability to adapt limits the application of tracking systems to only one field of view or a limited number of objects for PTZ cameras.